SlideShare a Scribd company logo
1 of 33
Download to read offline
The	Briefing	Room
Tuesday,	May	2,	2017	@	4	ET
Tweet	with	#BriefR
The Model Enterprise: A Blueprint for Enterprise Data Governance
Governance
• Carrots	&	Sticks
• Control	Points
• Pragmatism
• Durability
• Balancing	Act
• Transparency
• Enforceability
• Chinese	Handcuffs
1© 2017 IDERA, Inc. All rights reserved.
THE MODEL ENTERPRISE:
A BLUEPRINT FOR ENTERPRISE DATA GOVERNANCE
MAY 2, 2017
Ron Huizenga
Senior Product Manager, Enterprise Architecture & Modeling
@DataAviator
2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2017 IDERA, Inc. All rights reserved.
AGENDA
§ Governance Overview
§ Definitions
§ Master Data
§ Data lineage & life cycle
§ Master Data Management (MDM)
§ Importance of Data Models
§ Data quality
Data
Governance
Data
Architecture
Management
Data
Development
Database
Operations
Management
Data Security
Management
Reference &
Master Data
Management
Data
Warehousing
& Business
Intelligence
Management
Document &
Content
Management
Metadata
Management
Data Quality
Management
3© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 3© 2017 IDERA, Inc. All rights reserved.
ER/STUDIO ENTERPRISE TEAM EDITION 2016+
ER/Studio Software
Architect
ER/Studio Business
Architect
ER/Studio Repository
& Team Server
ER/Studio Data
Architect
4© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 4© 2017 IDERA, Inc. All rights reserved.
DMBOK: DEFINITIONS
§ Data Governance
• The exercise of authority, control and shared decision making (planning,
monitoring and enforcement) over the management of data assets.
§ Master Data
• Synonymous with reference data. The data that provides the context for
transaction data. It includes the details (definitions and identifiers) of internal
and external objects involved in business transactions. Includes data about
customers, products, employees, vendors, and controlled domains (code
values).
§ Master Data Management
• Processes that ensure that reference data is kept up to date and coordinated
across an enterprise. The organization, management and distribution of
corporately adjudicated data with widespread use in the organization.
5© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 5© 2017 IDERA, Inc. All rights reserved.
MASTER DATA CLASSIFICATION CONSIDERATIONS
§ Behavior
§ Life Cycle
§ Complexity
§ Value
§ Volatility
§ Reuse
6© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 6© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - BEHAVIOR
§ Can be described by the way it interacts with other data
§ Master data is almost always involved with transactional data
§ Often a noun/verb relationship between the master data item and the
transaction
• Master data are the nouns
• Customer
• Product
• Transactional data capture the verbs
• Customer places order
• Product sold on order
§ Same type of relationships are shared between facts and dimensions in a data
warehouse
• Master data are the dimensions
• Transactions are the facts
7© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 7© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - LIFECYCLE
§ Describes how a master data element is created, read, updated, deleted (CRUD)
§ Many factors come into play
• Business rules
• Business processes
• Applications
§ There may be more than 1 way a particular master data element is created
§ Need to model:
• Business process
• Data lineage
• Data flow
• Integration
• Include Extract Transform and Load (ETL) for data warehouse/data marts and staging
areas
8© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 8© 2017 IDERA, Inc. All rights reserved.
BUSINESS PROCESS & DATA CRUD
9© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 9© 2017 IDERA, Inc. All rights reserved.
MASTER DATA – COMPLEXITY, VALUE
§ Complexity
• Very simple entities are rarely a challenge to manage
• The less complex an element, the less likely the need to manage change
• Likely not master data elements
• Possibly reference data
− States/Provinces
− Units of measure
− Classification references
§ Value
• Value and complexity interact
• The higher value a data element is to an organization the more likely it will be
considered master data
10© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 10© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - VOLATILITY
§ Level of change in characteristics describing a master data element
• Frequent change = high volatility
• Infrequent change = low volatility
§ Sometimes referred to as stability
• Frequent change = unstable
• Infrequent change = stable
11© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 11© 2017 IDERA, Inc. All rights reserved.
MASTER DATA - REUSE
§ Master data elements are often shared across a number of systems
§ Can lead to inconsistency and errors
• Multiple systems
• Which is the “version of truth”
• Spreadsheets
• Private data stores
§ An error in master data can cause errors in
• All the transactions that use it
• All the applications that use it
• All reports and analytics that use it
§ This is one of the primary reasons for “Master Data Management”
12© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 12© 2017 IDERA, Inc. All rights reserved.
WHAT IS MASTER DATA MANAGEMENT?
§ The processes, tools and technology required to create and maintain consistent
and accurate lists of master data
§ Includes both creating and maintaining master data
§ Often requires fundamental changes in business process
§ Not just a technological problem
§ Some of the most difficult issues are more political than technical
§ Organization wide MDM may be difficult
• Many organizations begin with critical, high value elements
• Grow and expand
§ MDM is not a project
• Ongoing
• Continuous improvement
13© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 13© 2017 IDERA, Inc. All rights reserved.
MDM ACTIVITIES
§ Identify sources of master data
§ Identify the producers and
consumers of the master data
§ Collect and analyze metadata
about for your master data
§ Appoint data stewards
§ Implement a data-governance
program and council
§ Develop the master-data model
§ Choose a toolset
§ Design the infrastructure
§ Generate and test the master data
§ Modify the producing and
consuming systems
§ Be sure to incorporate versioning
and auditing
14© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 14© 2017 IDERA, Inc. All rights reserved.
IMPORTANCE OF DATA MODELS
§ Full Specification
• Logical
• Physical
§ Persistence Boundaries
• Business Data Objects
§ Descriptive metadata
• Names
• Definitions (data dictionary)
• Notes
§ Implementation characteristics
• Data types
• Keys
• Indexes
• Views
§ Business Rules
• Relationships (referential
constraints)
• Value Restrictions (constraints)
§ Security Classifications + Rules
§ Governance Metadata
• Master Data Management classes
• Data Quality classifications
• Retention policies
15© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 15© 2017 IDERA, Inc. All rights reserved.
DATA DICTIONARY – METADATA EXTENSIONS
16© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 16© 2017 IDERA, Inc. All rights reserved.
ER/STUDIO – METADATA ATTACHMENT SETUP
17© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 17© 2017 IDERA, Inc. All rights reserved.
UNIVERSAL MAPPINGS
18© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 18© 2017 IDERA, Inc. All rights reserved.
UNIVERSAL MAPPINGS
§ Ability to link “like” or related objects
• Within same model file
• Across separate model files
§ Entity/Table level
§ Attribute/Column level
19© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 19© 2017 IDERA, Inc. All rights reserved.
SUMMARY
§ Master Data Management is a critical aspect of Data Governance
§ Master Data Characteristics
• Behavior
• Lifecycle
• Complexity
• Volatility
• Reuse
§ MDM is an ongoing, continuous improvement discipline, not a project
§ Data models & metadata constitute the blueprint for data governance
§ Mapping the processes that utilize the data is imperative to defining the data life
cycle
§ Achieving data maturity is a journey
20© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 20© 2016 IDERA, Inc. All rights reserved.
THANKS!
Any questions?
You can find me at:
ron.huizenga@idera.com
@DataAviator
The Governance Game
Robin Bloor, PhD
Big Data Means Big Governance
The analytical opportunity of BIG
DATA is clear – there are already many
profitable uses
Nevertheless, all data needs to be
GOVERNED
The Data Governance Challenge
Data Sources
Metadata Management
Data meaning
Data compliance
Data provenance & lineage
Data cleansing
Data security
Data audit record
Data life-cycle mgt
Data Governance is a perpetual
process
The Growth of Compliance
u International
– GRC (Governance, Risk,
Compliance)
– ISO (standards)
u US Government:
– SOX
– GLBA
– HIPAA
– FISMA
– FERPA
u Europe
– GDPR (Data protection laws)
with variances
– New: The right to be forgotten
The Full Data Lake Picture
Data
Cleansing
Data
Security
Ingest
Metadata
Mgt
Real-Time
Apps
Transform &
Aggregate
Search &
Query
BI, Visual'n
& Analytics
Other
Apps
Data Lake
Mgt
Data
Governance
DATA LAKE
To
Databases
Data Marts
Other Apps
Archive
Life Cycle
Mgt Extracts
Servers, Desktops, Mobile, Network Devices, Embedded
Chips, RFID, IoT, The Cloud, Oses, VMs, Log Files, Sys
Mgt Apps, ESBs, Web Services, SaaS, Business Apps,
Office Apps, BI Apps, Workflow, Data Streams, Social...
The Need For Data Modeling & MDM
Points To Note
u The more complex the
data universe the more
you need a model.
u In theory it is a view of
the data universe. In
practice it is part of it.
u Beginning: Modeling is
top-down and bottom
up. You build in both
directions
u It is not and never can
be a project. It is an on-
going activity.
The Net Net
Because IT and data management is
evolving so quickly, governance and
data modeling must also evolve
quickly
u Agile modeling clearly requires effective
collaboration between all data users at every
level. How does your technology help with
cultural issues?
u Which data stores and databases do you
support aside form the usual relational
sources? (Hadoop, NoSQL, unstructured,
etc.)offer for NoSQL databases?
u How do you accommodate the IoT?
u If you do not do MDM already, how do you start
and what are the immediate business benefits?
u Do you model data flows (consider, for example,
real-time analytics)?
u Where do you see current/future competition
emerging from in the modeling or governance
market?

More Related Content

What's hot

TechEvent DWH Modernization
TechEvent DWH ModernizationTechEvent DWH Modernization
TechEvent DWH ModernizationTrivadis
 
seven steps to dataops @ dataops.rocks conference Oct 2019
seven steps to dataops @ dataops.rocks conference Oct 2019seven steps to dataops @ dataops.rocks conference Oct 2019
seven steps to dataops @ dataops.rocks conference Oct 2019DataKitchen
 
The Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubThe Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
 
Your Data Nerd Friends Need You!
Your Data Nerd Friends Need You!Your Data Nerd Friends Need You!
Your Data Nerd Friends Need You! DataKitchen
 
Moving to the Cloud: Modernizing Data Architecture in Healthcare
Moving to the Cloud: Modernizing Data Architecture in HealthcareMoving to the Cloud: Modernizing Data Architecture in Healthcare
Moving to the Cloud: Modernizing Data Architecture in HealthcarePerficient, Inc.
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?Caserta
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for EveryoneCaserta
 
Data Warehousing in the Cloud: Practical Migration Strategies
Data Warehousing in the Cloud: Practical Migration Strategies Data Warehousing in the Cloud: Practical Migration Strategies
Data Warehousing in the Cloud: Practical Migration Strategies SnapLogic
 
Company report xinglian
Company report xinglianCompany report xinglian
Company report xinglianXinglian Liu
 
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...Rehgan Avon
 
Piranha vs. mammoth predator appliances that chew up big data
Piranha vs. mammoth   predator appliances that chew up big dataPiranha vs. mammoth   predator appliances that chew up big data
Piranha vs. mammoth predator appliances that chew up big dataJack (Yaakov) Bezalel
 
A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovation...A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovation...DataWorks Summit
 
Big Data for Managers: From hadoop to streaming and beyond
Big Data for Managers: From hadoop to streaming and beyondBig Data for Managers: From hadoop to streaming and beyond
Big Data for Managers: From hadoop to streaming and beyondDataWorks Summit/Hadoop Summit
 
Continuous Data Replication into Cloud Storage with Oracle GoldenGate
Continuous Data Replication into Cloud Storage with Oracle GoldenGateContinuous Data Replication into Cloud Storage with Oracle GoldenGate
Continuous Data Replication into Cloud Storage with Oracle GoldenGateMichael Rainey
 
How to add security in dataops and devops
How to add security in dataops and devopsHow to add security in dataops and devops
How to add security in dataops and devopsUlf Mattsson
 
Developing a Strategy for Data Lake Governance
Developing a Strategy for Data Lake GovernanceDeveloping a Strategy for Data Lake Governance
Developing a Strategy for Data Lake GovernanceTony Baer
 
The Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubThe Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
 
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain Pipeline
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineThe Zen of DataOps – AWS Lake Formation and the Data Supply Chain Pipeline
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
 
2020 Big Data & Analytics Maturity Survey Results
2020 Big Data & Analytics Maturity Survey Results2020 Big Data & Analytics Maturity Survey Results
2020 Big Data & Analytics Maturity Survey ResultsAtScale
 
Low-tech, Low-cost data management: Six insights from national reporting on f...
Low-tech, Low-cost data management: Six insights from national reporting on f...Low-tech, Low-cost data management: Six insights from national reporting on f...
Low-tech, Low-cost data management: Six insights from national reporting on f...srjbridge
 

What's hot (20)

TechEvent DWH Modernization
TechEvent DWH ModernizationTechEvent DWH Modernization
TechEvent DWH Modernization
 
seven steps to dataops @ dataops.rocks conference Oct 2019
seven steps to dataops @ dataops.rocks conference Oct 2019seven steps to dataops @ dataops.rocks conference Oct 2019
seven steps to dataops @ dataops.rocks conference Oct 2019
 
The Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubThe Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data Hub
 
Your Data Nerd Friends Need You!
Your Data Nerd Friends Need You!Your Data Nerd Friends Need You!
Your Data Nerd Friends Need You!
 
Moving to the Cloud: Modernizing Data Architecture in Healthcare
Moving to the Cloud: Modernizing Data Architecture in HealthcareMoving to the Cloud: Modernizing Data Architecture in Healthcare
Moving to the Cloud: Modernizing Data Architecture in Healthcare
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for Everyone
 
Data Warehousing in the Cloud: Practical Migration Strategies
Data Warehousing in the Cloud: Practical Migration Strategies Data Warehousing in the Cloud: Practical Migration Strategies
Data Warehousing in the Cloud: Practical Migration Strategies
 
Company report xinglian
Company report xinglianCompany report xinglian
Company report xinglian
 
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...
Kelly O'Briant - DataOps in the Cloud: How To Supercharge Data Science with a...
 
Piranha vs. mammoth predator appliances that chew up big data
Piranha vs. mammoth   predator appliances that chew up big dataPiranha vs. mammoth   predator appliances that chew up big data
Piranha vs. mammoth predator appliances that chew up big data
 
A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovation...A modern, flexible approach to Hadoop implementation incorporating innovation...
A modern, flexible approach to Hadoop implementation incorporating innovation...
 
Big Data for Managers: From hadoop to streaming and beyond
Big Data for Managers: From hadoop to streaming and beyondBig Data for Managers: From hadoop to streaming and beyond
Big Data for Managers: From hadoop to streaming and beyond
 
Continuous Data Replication into Cloud Storage with Oracle GoldenGate
Continuous Data Replication into Cloud Storage with Oracle GoldenGateContinuous Data Replication into Cloud Storage with Oracle GoldenGate
Continuous Data Replication into Cloud Storage with Oracle GoldenGate
 
How to add security in dataops and devops
How to add security in dataops and devopsHow to add security in dataops and devops
How to add security in dataops and devops
 
Developing a Strategy for Data Lake Governance
Developing a Strategy for Data Lake GovernanceDeveloping a Strategy for Data Lake Governance
Developing a Strategy for Data Lake Governance
 
The Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data HubThe Future of Data Management: The Enterprise Data Hub
The Future of Data Management: The Enterprise Data Hub
 
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain Pipeline
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineThe Zen of DataOps – AWS Lake Formation and the Data Supply Chain Pipeline
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain Pipeline
 
2020 Big Data & Analytics Maturity Survey Results
2020 Big Data & Analytics Maturity Survey Results2020 Big Data & Analytics Maturity Survey Results
2020 Big Data & Analytics Maturity Survey Results
 
Low-tech, Low-cost data management: Six insights from national reporting on f...
Low-tech, Low-cost data management: Six insights from national reporting on f...Low-tech, Low-cost data management: Six insights from national reporting on f...
Low-tech, Low-cost data management: Six insights from national reporting on f...
 

Similar to The Model Enterprise: A Blueprint for Enterprise Data Governance

IDERA Live | Databases Don't Build and Populate Themselves
IDERA Live | Databases Don't Build and Populate ThemselvesIDERA Live | Databases Don't Build and Populate Themselves
IDERA Live | Databases Don't Build and Populate ThemselvesIDERA Software
 
IDERA Live | Maintaining Data Governance During Rapidly Changing Conditions
IDERA Live | Maintaining Data Governance During Rapidly Changing ConditionsIDERA Live | Maintaining Data Governance During Rapidly Changing Conditions
IDERA Live | Maintaining Data Governance During Rapidly Changing ConditionsIDERA Software
 
IDERA Live | Decode your Organization's Data DNA
IDERA Live | Decode your Organization's Data DNAIDERA Live | Decode your Organization's Data DNA
IDERA Live | Decode your Organization's Data DNAIDERA Software
 
Integrate ERP and CRM Metadata into ER/Studio
Integrate ERP and CRM Metadata into ER/StudioIntegrate ERP and CRM Metadata into ER/Studio
Integrate ERP and CRM Metadata into ER/StudioDATAVERSITY
 
Strategic imperative the enterprise data model
Strategic imperative the enterprise data modelStrategic imperative the enterprise data model
Strategic imperative the enterprise data modelDATAVERSITY
 
Straight Talk to Demystify Data Lineage
Straight Talk to Demystify Data LineageStraight Talk to Demystify Data Lineage
Straight Talk to Demystify Data LineageDATAVERSITY
 
Data Management for High Performance Analytics
Data Management for High Performance AnalyticsData Management for High Performance Analytics
Data Management for High Performance AnalyticsMary Snyder
 
Balancing Data Governance and Innovation
Balancing Data Governance and InnovationBalancing Data Governance and Innovation
Balancing Data Governance and InnovationCaserta
 
The Emerging Role of the Data Lake
The Emerging Role of the Data LakeThe Emerging Role of the Data Lake
The Emerging Role of the Data LakeCaserta
 
Balancing Data Governance and Innovation
Balancing Data Governance and InnovationBalancing Data Governance and Innovation
Balancing Data Governance and InnovationCaserta
 
Managing Data Warehouse Growth in the New Era of Big Data
Managing Data Warehouse Growth in the New Era of Big DataManaging Data Warehouse Growth in the New Era of Big Data
Managing Data Warehouse Growth in the New Era of Big DataVineet
 
Data Maturity - A Balanced Approach
Data Maturity - A Balanced ApproachData Maturity - A Balanced Approach
Data Maturity - A Balanced ApproachDATAVERSITY
 
What Data Do You Have and Where is It?
What Data Do You Have and Where is It? What Data Do You Have and Where is It?
What Data Do You Have and Where is It? Caserta
 
Data Management, Metadata Management, and Data Governance – Working Together
Data Management, Metadata Management, and Data Governance – Working TogetherData Management, Metadata Management, and Data Governance – Working Together
Data Management, Metadata Management, and Data Governance – Working TogetherDATAVERSITY
 
All Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceAll Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceInside Analysis
 
Slides: The Business Value of Data Modeling
Slides: The Business Value of Data ModelingSlides: The Business Value of Data Modeling
Slides: The Business Value of Data ModelingDATAVERSITY
 
Mastering Data Modeling for NoSQL Platforms
Mastering Data Modeling for NoSQL PlatformsMastering Data Modeling for NoSQL Platforms
Mastering Data Modeling for NoSQL PlatformsDATAVERSITY
 
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DATAVERSITY
 
The Importance of Master Data Management
The Importance of Master Data ManagementThe Importance of Master Data Management
The Importance of Master Data ManagementDATAVERSITY
 
Oracle Big Data Governance Webcast Charts
Oracle Big Data Governance Webcast ChartsOracle Big Data Governance Webcast Charts
Oracle Big Data Governance Webcast ChartsJeffrey T. Pollock
 

Similar to The Model Enterprise: A Blueprint for Enterprise Data Governance (20)

IDERA Live | Databases Don't Build and Populate Themselves
IDERA Live | Databases Don't Build and Populate ThemselvesIDERA Live | Databases Don't Build and Populate Themselves
IDERA Live | Databases Don't Build and Populate Themselves
 
IDERA Live | Maintaining Data Governance During Rapidly Changing Conditions
IDERA Live | Maintaining Data Governance During Rapidly Changing ConditionsIDERA Live | Maintaining Data Governance During Rapidly Changing Conditions
IDERA Live | Maintaining Data Governance During Rapidly Changing Conditions
 
IDERA Live | Decode your Organization's Data DNA
IDERA Live | Decode your Organization's Data DNAIDERA Live | Decode your Organization's Data DNA
IDERA Live | Decode your Organization's Data DNA
 
Integrate ERP and CRM Metadata into ER/Studio
Integrate ERP and CRM Metadata into ER/StudioIntegrate ERP and CRM Metadata into ER/Studio
Integrate ERP and CRM Metadata into ER/Studio
 
Strategic imperative the enterprise data model
Strategic imperative the enterprise data modelStrategic imperative the enterprise data model
Strategic imperative the enterprise data model
 
Straight Talk to Demystify Data Lineage
Straight Talk to Demystify Data LineageStraight Talk to Demystify Data Lineage
Straight Talk to Demystify Data Lineage
 
Data Management for High Performance Analytics
Data Management for High Performance AnalyticsData Management for High Performance Analytics
Data Management for High Performance Analytics
 
Balancing Data Governance and Innovation
Balancing Data Governance and InnovationBalancing Data Governance and Innovation
Balancing Data Governance and Innovation
 
The Emerging Role of the Data Lake
The Emerging Role of the Data LakeThe Emerging Role of the Data Lake
The Emerging Role of the Data Lake
 
Balancing Data Governance and Innovation
Balancing Data Governance and InnovationBalancing Data Governance and Innovation
Balancing Data Governance and Innovation
 
Managing Data Warehouse Growth in the New Era of Big Data
Managing Data Warehouse Growth in the New Era of Big DataManaging Data Warehouse Growth in the New Era of Big Data
Managing Data Warehouse Growth in the New Era of Big Data
 
Data Maturity - A Balanced Approach
Data Maturity - A Balanced ApproachData Maturity - A Balanced Approach
Data Maturity - A Balanced Approach
 
What Data Do You Have and Where is It?
What Data Do You Have and Where is It? What Data Do You Have and Where is It?
What Data Do You Have and Where is It?
 
Data Management, Metadata Management, and Data Governance – Working Together
Data Management, Metadata Management, and Data Governance – Working TogetherData Management, Metadata Management, and Data Governance – Working Together
Data Management, Metadata Management, and Data Governance – Working Together
 
All Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data GovernanceAll Together Now: A Recipe for Successful Data Governance
All Together Now: A Recipe for Successful Data Governance
 
Slides: The Business Value of Data Modeling
Slides: The Business Value of Data ModelingSlides: The Business Value of Data Modeling
Slides: The Business Value of Data Modeling
 
Mastering Data Modeling for NoSQL Platforms
Mastering Data Modeling for NoSQL PlatformsMastering Data Modeling for NoSQL Platforms
Mastering Data Modeling for NoSQL Platforms
 
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...DAS Slides: Metadata Management From Technical Architecture & Business Techni...
DAS Slides: Metadata Management From Technical Architecture & Business Techni...
 
The Importance of Master Data Management
The Importance of Master Data ManagementThe Importance of Master Data Management
The Importance of Master Data Management
 
Oracle Big Data Governance Webcast Charts
Oracle Big Data Governance Webcast ChartsOracle Big Data Governance Webcast Charts
Oracle Big Data Governance Webcast Charts
 

More from Eric Kavanagh

Best Practices in DataOps: How to Create Agile, Automated Data Pipelines
Best Practices in DataOps: How to Create Agile, Automated Data PipelinesBest Practices in DataOps: How to Create Agile, Automated Data Pipelines
Best Practices in DataOps: How to Create Agile, Automated Data PipelinesEric Kavanagh
 
Expediting the Path to Discovery with Multi-Source Analysis
Expediting the Path to Discovery with Multi-Source AnalysisExpediting the Path to Discovery with Multi-Source Analysis
Expediting the Path to Discovery with Multi-Source AnalysisEric Kavanagh
 
Will AI Eliminate Reports and Dashboards
Will AI Eliminate Reports and DashboardsWill AI Eliminate Reports and Dashboards
Will AI Eliminate Reports and DashboardsEric Kavanagh
 
Database Survival Guide: Exploratory Webcast
Database Survival Guide: Exploratory WebcastDatabase Survival Guide: Exploratory Webcast
Database Survival Guide: Exploratory WebcastEric Kavanagh
 
Better to Ask Permission? Best Practices for Privacy and Security
Better to Ask Permission? Best Practices for Privacy and SecurityBetter to Ask Permission? Best Practices for Privacy and Security
Better to Ask Permission? Best Practices for Privacy and SecurityEric Kavanagh
 
Best Laid Plans: Saving Time, Money and Trouble with Optimal Forecasting
Best Laid Plans: Saving Time, Money and Trouble with Optimal ForecastingBest Laid Plans: Saving Time, Money and Trouble with Optimal Forecasting
Best Laid Plans: Saving Time, Money and Trouble with Optimal ForecastingEric Kavanagh
 
A Winning Strategy for the Digital Economy
A Winning Strategy for the Digital EconomyA Winning Strategy for the Digital Economy
A Winning Strategy for the Digital EconomyEric Kavanagh
 
Discovering Big Data in the Fog: Why Catalogs Matter
 Discovering Big Data in the Fog: Why Catalogs Matter Discovering Big Data in the Fog: Why Catalogs Matter
Discovering Big Data in the Fog: Why Catalogs MatterEric Kavanagh
 
Health Check: Maintaining Enterprise BI
Health Check: Maintaining Enterprise BIHealth Check: Maintaining Enterprise BI
Health Check: Maintaining Enterprise BIEric Kavanagh
 
Rapid Response: Debugging and Profiling to the Rescue
Rapid Response: Debugging and Profiling to the RescueRapid Response: Debugging and Profiling to the Rescue
Rapid Response: Debugging and Profiling to the RescueEric Kavanagh
 
Solving the Really Big Tech Problems with IoT
 Solving the Really Big Tech Problems with IoT Solving the Really Big Tech Problems with IoT
Solving the Really Big Tech Problems with IoTEric Kavanagh
 
Beyond the Platform: Enabling Fluid Analysis
Beyond the Platform: Enabling Fluid AnalysisBeyond the Platform: Enabling Fluid Analysis
Beyond the Platform: Enabling Fluid AnalysisEric Kavanagh
 
Protect Your Database: High Availability for High Demand Data
 Protect Your Database: High Availability for High Demand Data Protect Your Database: High Availability for High Demand Data
Protect Your Database: High Availability for High Demand DataEric Kavanagh
 
A Better Understanding: Solving Business Challenges with Data
A Better Understanding: Solving Business Challenges with DataA Better Understanding: Solving Business Challenges with Data
A Better Understanding: Solving Business Challenges with DataEric Kavanagh
 
The Key to Effective Analytics: Fast-Returning Queries
The Key to Effective Analytics: Fast-Returning QueriesThe Key to Effective Analytics: Fast-Returning Queries
The Key to Effective Analytics: Fast-Returning QueriesEric Kavanagh
 
A Tight Ship: How Containers and SDS Optimize the Enterprise
 A Tight Ship: How Containers and SDS Optimize the Enterprise A Tight Ship: How Containers and SDS Optimize the Enterprise
A Tight Ship: How Containers and SDS Optimize the EnterpriseEric Kavanagh
 
Application Acceleration: Faster Performance for End Users
Application Acceleration: Faster Performance for End Users	Application Acceleration: Faster Performance for End Users
Application Acceleration: Faster Performance for End Users Eric Kavanagh
 
Time's Up! Getting Value from Big Data Now
Time's Up! Getting Value from Big Data NowTime's Up! Getting Value from Big Data Now
Time's Up! Getting Value from Big Data NowEric Kavanagh
 
The New Normal: Dealing with the Reality of an Unsecure World
The New Normal: Dealing with the Reality of an Unsecure WorldThe New Normal: Dealing with the Reality of an Unsecure World
The New Normal: Dealing with the Reality of an Unsecure WorldEric Kavanagh
 
The Central Hub: Defining the Data Lake
The Central Hub: Defining the Data LakeThe Central Hub: Defining the Data Lake
The Central Hub: Defining the Data LakeEric Kavanagh
 

More from Eric Kavanagh (20)

Best Practices in DataOps: How to Create Agile, Automated Data Pipelines
Best Practices in DataOps: How to Create Agile, Automated Data PipelinesBest Practices in DataOps: How to Create Agile, Automated Data Pipelines
Best Practices in DataOps: How to Create Agile, Automated Data Pipelines
 
Expediting the Path to Discovery with Multi-Source Analysis
Expediting the Path to Discovery with Multi-Source AnalysisExpediting the Path to Discovery with Multi-Source Analysis
Expediting the Path to Discovery with Multi-Source Analysis
 
Will AI Eliminate Reports and Dashboards
Will AI Eliminate Reports and DashboardsWill AI Eliminate Reports and Dashboards
Will AI Eliminate Reports and Dashboards
 
Database Survival Guide: Exploratory Webcast
Database Survival Guide: Exploratory WebcastDatabase Survival Guide: Exploratory Webcast
Database Survival Guide: Exploratory Webcast
 
Better to Ask Permission? Best Practices for Privacy and Security
Better to Ask Permission? Best Practices for Privacy and SecurityBetter to Ask Permission? Best Practices for Privacy and Security
Better to Ask Permission? Best Practices for Privacy and Security
 
Best Laid Plans: Saving Time, Money and Trouble with Optimal Forecasting
Best Laid Plans: Saving Time, Money and Trouble with Optimal ForecastingBest Laid Plans: Saving Time, Money and Trouble with Optimal Forecasting
Best Laid Plans: Saving Time, Money and Trouble with Optimal Forecasting
 
A Winning Strategy for the Digital Economy
A Winning Strategy for the Digital EconomyA Winning Strategy for the Digital Economy
A Winning Strategy for the Digital Economy
 
Discovering Big Data in the Fog: Why Catalogs Matter
 Discovering Big Data in the Fog: Why Catalogs Matter Discovering Big Data in the Fog: Why Catalogs Matter
Discovering Big Data in the Fog: Why Catalogs Matter
 
Health Check: Maintaining Enterprise BI
Health Check: Maintaining Enterprise BIHealth Check: Maintaining Enterprise BI
Health Check: Maintaining Enterprise BI
 
Rapid Response: Debugging and Profiling to the Rescue
Rapid Response: Debugging and Profiling to the RescueRapid Response: Debugging and Profiling to the Rescue
Rapid Response: Debugging and Profiling to the Rescue
 
Solving the Really Big Tech Problems with IoT
 Solving the Really Big Tech Problems with IoT Solving the Really Big Tech Problems with IoT
Solving the Really Big Tech Problems with IoT
 
Beyond the Platform: Enabling Fluid Analysis
Beyond the Platform: Enabling Fluid AnalysisBeyond the Platform: Enabling Fluid Analysis
Beyond the Platform: Enabling Fluid Analysis
 
Protect Your Database: High Availability for High Demand Data
 Protect Your Database: High Availability for High Demand Data Protect Your Database: High Availability for High Demand Data
Protect Your Database: High Availability for High Demand Data
 
A Better Understanding: Solving Business Challenges with Data
A Better Understanding: Solving Business Challenges with DataA Better Understanding: Solving Business Challenges with Data
A Better Understanding: Solving Business Challenges with Data
 
The Key to Effective Analytics: Fast-Returning Queries
The Key to Effective Analytics: Fast-Returning QueriesThe Key to Effective Analytics: Fast-Returning Queries
The Key to Effective Analytics: Fast-Returning Queries
 
A Tight Ship: How Containers and SDS Optimize the Enterprise
 A Tight Ship: How Containers and SDS Optimize the Enterprise A Tight Ship: How Containers and SDS Optimize the Enterprise
A Tight Ship: How Containers and SDS Optimize the Enterprise
 
Application Acceleration: Faster Performance for End Users
Application Acceleration: Faster Performance for End Users	Application Acceleration: Faster Performance for End Users
Application Acceleration: Faster Performance for End Users
 
Time's Up! Getting Value from Big Data Now
Time's Up! Getting Value from Big Data NowTime's Up! Getting Value from Big Data Now
Time's Up! Getting Value from Big Data Now
 
The New Normal: Dealing with the Reality of an Unsecure World
The New Normal: Dealing with the Reality of an Unsecure WorldThe New Normal: Dealing with the Reality of an Unsecure World
The New Normal: Dealing with the Reality of an Unsecure World
 
The Central Hub: Defining the Data Lake
The Central Hub: Defining the Data LakeThe Central Hub: Defining the Data Lake
The Central Hub: Defining the Data Lake
 

Recently uploaded

UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISING
UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISINGUNLEASHING THE POWER OF PROGRAMMATIC ADVERTISING
UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISINGlokeshwarmaha
 
Team B Mind Map for Organizational Chg..
Team B Mind Map for Organizational Chg..Team B Mind Map for Organizational Chg..
Team B Mind Map for Organizational Chg..dlewis191
 
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003believeminhh
 
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...Brian Solis
 
MoneyBridge Pitch Deck - Investor Presentation
MoneyBridge Pitch Deck - Investor PresentationMoneyBridge Pitch Deck - Investor Presentation
MoneyBridge Pitch Deck - Investor Presentationbaron83
 
Building Your Personal Brand on LinkedIn - Expert Planet- 2024
 Building Your Personal Brand on LinkedIn - Expert Planet-  2024 Building Your Personal Brand on LinkedIn - Expert Planet-  2024
Building Your Personal Brand on LinkedIn - Expert Planet- 2024Stephan Koning
 
A flour, rice and Suji company in Jhang.
A flour, rice and Suji company in Jhang.A flour, rice and Suji company in Jhang.
A flour, rice and Suji company in Jhang.mcshagufta46
 
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)tazeenaila12
 
Plano de marketing- inglês em formato ppt
Plano de marketing- inglês  em formato pptPlano de marketing- inglês  em formato ppt
Plano de marketing- inglês em formato pptElizangelaSoaresdaCo
 
Slicing Work on Business Agility Meetup Berlin
Slicing Work on Business Agility Meetup BerlinSlicing Work on Business Agility Meetup Berlin
Slicing Work on Business Agility Meetup BerlinAnton Skornyakov
 
Mihir Menda - Member of Supervisory Board at RMZ
Mihir Menda - Member of Supervisory Board at RMZMihir Menda - Member of Supervisory Board at RMZ
Mihir Menda - Member of Supervisory Board at RMZKanakChauhan5
 
Borderless Access - Global B2B Panel book-unlock 2024
Borderless Access - Global B2B Panel book-unlock 2024Borderless Access - Global B2B Panel book-unlock 2024
Borderless Access - Global B2B Panel book-unlock 2024Borderless Access
 
Entrepreneurship & organisations: influences and organizations
Entrepreneurship & organisations: influences and organizationsEntrepreneurship & organisations: influences and organizations
Entrepreneurship & organisations: influences and organizationsP&CO
 
Michael Vidyakin: Introduction to PMO (UA)
Michael Vidyakin: Introduction to PMO (UA)Michael Vidyakin: Introduction to PMO (UA)
Michael Vidyakin: Introduction to PMO (UA)Lviv Startup Club
 
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...AustraliaChapterIIBA
 
Lecture_6.pptx English speaking easyb to
Lecture_6.pptx English speaking easyb toLecture_6.pptx English speaking easyb to
Lecture_6.pptx English speaking easyb toumarfarooquejamali32
 
Tata Kelola Bisnis perushaan yang bergerak
Tata Kelola Bisnis perushaan yang bergerakTata Kelola Bisnis perushaan yang bergerak
Tata Kelola Bisnis perushaan yang bergerakEditores1
 
Borderless Access - Global Panel book-unlock 2024
Borderless Access - Global Panel book-unlock 2024Borderless Access - Global Panel book-unlock 2024
Borderless Access - Global Panel book-unlock 2024Borderless Access
 
Introduction to The overview of GAAP LO 1-5.pptx
Introduction to The overview of GAAP LO 1-5.pptxIntroduction to The overview of GAAP LO 1-5.pptx
Introduction to The overview of GAAP LO 1-5.pptxJemalSeid25
 

Recently uploaded (20)

UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISING
UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISINGUNLEASHING THE POWER OF PROGRAMMATIC ADVERTISING
UNLEASHING THE POWER OF PROGRAMMATIC ADVERTISING
 
Team B Mind Map for Organizational Chg..
Team B Mind Map for Organizational Chg..Team B Mind Map for Organizational Chg..
Team B Mind Map for Organizational Chg..
 
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003
The Vietnam Believer Newsletter_MARCH 25, 2024_EN_Vol. 003
 
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...
The End of Business as Usual: Rewire the Way You Work to Succeed in the Consu...
 
MoneyBridge Pitch Deck - Investor Presentation
MoneyBridge Pitch Deck - Investor PresentationMoneyBridge Pitch Deck - Investor Presentation
MoneyBridge Pitch Deck - Investor Presentation
 
Building Your Personal Brand on LinkedIn - Expert Planet- 2024
 Building Your Personal Brand on LinkedIn - Expert Planet-  2024 Building Your Personal Brand on LinkedIn - Expert Planet-  2024
Building Your Personal Brand on LinkedIn - Expert Planet- 2024
 
A flour, rice and Suji company in Jhang.
A flour, rice and Suji company in Jhang.A flour, rice and Suji company in Jhang.
A flour, rice and Suji company in Jhang.
 
WAM Corporate Presentation Mar 25 2024.pdf
WAM Corporate Presentation Mar 25 2024.pdfWAM Corporate Presentation Mar 25 2024.pdf
WAM Corporate Presentation Mar 25 2024.pdf
 
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)
Harvard Business Review.pptx | Navigating Labor Unrest (March-April 2024)
 
Plano de marketing- inglês em formato ppt
Plano de marketing- inglês  em formato pptPlano de marketing- inglês  em formato ppt
Plano de marketing- inglês em formato ppt
 
Slicing Work on Business Agility Meetup Berlin
Slicing Work on Business Agility Meetup BerlinSlicing Work on Business Agility Meetup Berlin
Slicing Work on Business Agility Meetup Berlin
 
Mihir Menda - Member of Supervisory Board at RMZ
Mihir Menda - Member of Supervisory Board at RMZMihir Menda - Member of Supervisory Board at RMZ
Mihir Menda - Member of Supervisory Board at RMZ
 
Borderless Access - Global B2B Panel book-unlock 2024
Borderless Access - Global B2B Panel book-unlock 2024Borderless Access - Global B2B Panel book-unlock 2024
Borderless Access - Global B2B Panel book-unlock 2024
 
Entrepreneurship & organisations: influences and organizations
Entrepreneurship & organisations: influences and organizationsEntrepreneurship & organisations: influences and organizations
Entrepreneurship & organisations: influences and organizations
 
Michael Vidyakin: Introduction to PMO (UA)
Michael Vidyakin: Introduction to PMO (UA)Michael Vidyakin: Introduction to PMO (UA)
Michael Vidyakin: Introduction to PMO (UA)
 
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...
IIBA® Melbourne - Navigating Business Analysis - Excellence for Career Growth...
 
Lecture_6.pptx English speaking easyb to
Lecture_6.pptx English speaking easyb toLecture_6.pptx English speaking easyb to
Lecture_6.pptx English speaking easyb to
 
Tata Kelola Bisnis perushaan yang bergerak
Tata Kelola Bisnis perushaan yang bergerakTata Kelola Bisnis perushaan yang bergerak
Tata Kelola Bisnis perushaan yang bergerak
 
Borderless Access - Global Panel book-unlock 2024
Borderless Access - Global Panel book-unlock 2024Borderless Access - Global Panel book-unlock 2024
Borderless Access - Global Panel book-unlock 2024
 
Introduction to The overview of GAAP LO 1-5.pptx
Introduction to The overview of GAAP LO 1-5.pptxIntroduction to The overview of GAAP LO 1-5.pptx
Introduction to The overview of GAAP LO 1-5.pptx
 

The Model Enterprise: A Blueprint for Enterprise Data Governance

  • 3. Governance • Carrots & Sticks • Control Points • Pragmatism • Durability • Balancing Act • Transparency • Enforceability • Chinese Handcuffs
  • 4. 1© 2017 IDERA, Inc. All rights reserved. THE MODEL ENTERPRISE: A BLUEPRINT FOR ENTERPRISE DATA GOVERNANCE MAY 2, 2017 Ron Huizenga Senior Product Manager, Enterprise Architecture & Modeling @DataAviator
  • 5. 2© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 2© 2017 IDERA, Inc. All rights reserved. AGENDA § Governance Overview § Definitions § Master Data § Data lineage & life cycle § Master Data Management (MDM) § Importance of Data Models § Data quality Data Governance Data Architecture Management Data Development Database Operations Management Data Security Management Reference & Master Data Management Data Warehousing & Business Intelligence Management Document & Content Management Metadata Management Data Quality Management
  • 6. 3© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 3© 2017 IDERA, Inc. All rights reserved. ER/STUDIO ENTERPRISE TEAM EDITION 2016+ ER/Studio Software Architect ER/Studio Business Architect ER/Studio Repository & Team Server ER/Studio Data Architect
  • 7. 4© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 4© 2017 IDERA, Inc. All rights reserved. DMBOK: DEFINITIONS § Data Governance • The exercise of authority, control and shared decision making (planning, monitoring and enforcement) over the management of data assets. § Master Data • Synonymous with reference data. The data that provides the context for transaction data. It includes the details (definitions and identifiers) of internal and external objects involved in business transactions. Includes data about customers, products, employees, vendors, and controlled domains (code values). § Master Data Management • Processes that ensure that reference data is kept up to date and coordinated across an enterprise. The organization, management and distribution of corporately adjudicated data with widespread use in the organization.
  • 8. 5© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 5© 2017 IDERA, Inc. All rights reserved. MASTER DATA CLASSIFICATION CONSIDERATIONS § Behavior § Life Cycle § Complexity § Value § Volatility § Reuse
  • 9. 6© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 6© 2017 IDERA, Inc. All rights reserved. MASTER DATA - BEHAVIOR § Can be described by the way it interacts with other data § Master data is almost always involved with transactional data § Often a noun/verb relationship between the master data item and the transaction • Master data are the nouns • Customer • Product • Transactional data capture the verbs • Customer places order • Product sold on order § Same type of relationships are shared between facts and dimensions in a data warehouse • Master data are the dimensions • Transactions are the facts
  • 10. 7© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 7© 2017 IDERA, Inc. All rights reserved. MASTER DATA - LIFECYCLE § Describes how a master data element is created, read, updated, deleted (CRUD) § Many factors come into play • Business rules • Business processes • Applications § There may be more than 1 way a particular master data element is created § Need to model: • Business process • Data lineage • Data flow • Integration • Include Extract Transform and Load (ETL) for data warehouse/data marts and staging areas
  • 11. 8© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 8© 2017 IDERA, Inc. All rights reserved. BUSINESS PROCESS & DATA CRUD
  • 12. 9© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 9© 2017 IDERA, Inc. All rights reserved. MASTER DATA – COMPLEXITY, VALUE § Complexity • Very simple entities are rarely a challenge to manage • The less complex an element, the less likely the need to manage change • Likely not master data elements • Possibly reference data − States/Provinces − Units of measure − Classification references § Value • Value and complexity interact • The higher value a data element is to an organization the more likely it will be considered master data
  • 13. 10© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 10© 2017 IDERA, Inc. All rights reserved. MASTER DATA - VOLATILITY § Level of change in characteristics describing a master data element • Frequent change = high volatility • Infrequent change = low volatility § Sometimes referred to as stability • Frequent change = unstable • Infrequent change = stable
  • 14. 11© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 11© 2017 IDERA, Inc. All rights reserved. MASTER DATA - REUSE § Master data elements are often shared across a number of systems § Can lead to inconsistency and errors • Multiple systems • Which is the “version of truth” • Spreadsheets • Private data stores § An error in master data can cause errors in • All the transactions that use it • All the applications that use it • All reports and analytics that use it § This is one of the primary reasons for “Master Data Management”
  • 15. 12© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 12© 2017 IDERA, Inc. All rights reserved. WHAT IS MASTER DATA MANAGEMENT? § The processes, tools and technology required to create and maintain consistent and accurate lists of master data § Includes both creating and maintaining master data § Often requires fundamental changes in business process § Not just a technological problem § Some of the most difficult issues are more political than technical § Organization wide MDM may be difficult • Many organizations begin with critical, high value elements • Grow and expand § MDM is not a project • Ongoing • Continuous improvement
  • 16. 13© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 13© 2017 IDERA, Inc. All rights reserved. MDM ACTIVITIES § Identify sources of master data § Identify the producers and consumers of the master data § Collect and analyze metadata about for your master data § Appoint data stewards § Implement a data-governance program and council § Develop the master-data model § Choose a toolset § Design the infrastructure § Generate and test the master data § Modify the producing and consuming systems § Be sure to incorporate versioning and auditing
  • 17. 14© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 14© 2017 IDERA, Inc. All rights reserved. IMPORTANCE OF DATA MODELS § Full Specification • Logical • Physical § Persistence Boundaries • Business Data Objects § Descriptive metadata • Names • Definitions (data dictionary) • Notes § Implementation characteristics • Data types • Keys • Indexes • Views § Business Rules • Relationships (referential constraints) • Value Restrictions (constraints) § Security Classifications + Rules § Governance Metadata • Master Data Management classes • Data Quality classifications • Retention policies
  • 18. 15© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 15© 2017 IDERA, Inc. All rights reserved. DATA DICTIONARY – METADATA EXTENSIONS
  • 19. 16© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 16© 2017 IDERA, Inc. All rights reserved. ER/STUDIO – METADATA ATTACHMENT SETUP
  • 20. 17© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 17© 2017 IDERA, Inc. All rights reserved. UNIVERSAL MAPPINGS
  • 21. 18© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 18© 2017 IDERA, Inc. All rights reserved. UNIVERSAL MAPPINGS § Ability to link “like” or related objects • Within same model file • Across separate model files § Entity/Table level § Attribute/Column level
  • 22. 19© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 19© 2017 IDERA, Inc. All rights reserved. SUMMARY § Master Data Management is a critical aspect of Data Governance § Master Data Characteristics • Behavior • Lifecycle • Complexity • Volatility • Reuse § MDM is an ongoing, continuous improvement discipline, not a project § Data models & metadata constitute the blueprint for data governance § Mapping the processes that utilize the data is imperative to defining the data life cycle § Achieving data maturity is a journey
  • 23. 20© 2016 IDERA, Inc. All rights reserved. Proprietary and confidential. 20© 2016 IDERA, Inc. All rights reserved. THANKS! Any questions? You can find me at: ron.huizenga@idera.com @DataAviator
  • 25. Big Data Means Big Governance The analytical opportunity of BIG DATA is clear – there are already many profitable uses Nevertheless, all data needs to be GOVERNED
  • 26. The Data Governance Challenge Data Sources Metadata Management Data meaning Data compliance Data provenance & lineage Data cleansing Data security Data audit record Data life-cycle mgt Data Governance is a perpetual process
  • 27. The Growth of Compliance u International – GRC (Governance, Risk, Compliance) – ISO (standards) u US Government: – SOX – GLBA – HIPAA – FISMA – FERPA u Europe – GDPR (Data protection laws) with variances – New: The right to be forgotten
  • 28. The Full Data Lake Picture Data Cleansing Data Security Ingest Metadata Mgt Real-Time Apps Transform & Aggregate Search & Query BI, Visual'n & Analytics Other Apps Data Lake Mgt Data Governance DATA LAKE To Databases Data Marts Other Apps Archive Life Cycle Mgt Extracts Servers, Desktops, Mobile, Network Devices, Embedded Chips, RFID, IoT, The Cloud, Oses, VMs, Log Files, Sys Mgt Apps, ESBs, Web Services, SaaS, Business Apps, Office Apps, BI Apps, Workflow, Data Streams, Social...
  • 29. The Need For Data Modeling & MDM
  • 30. Points To Note u The more complex the data universe the more you need a model. u In theory it is a view of the data universe. In practice it is part of it. u Beginning: Modeling is top-down and bottom up. You build in both directions u It is not and never can be a project. It is an on- going activity.
  • 31. The Net Net Because IT and data management is evolving so quickly, governance and data modeling must also evolve quickly
  • 32. u Agile modeling clearly requires effective collaboration between all data users at every level. How does your technology help with cultural issues? u Which data stores and databases do you support aside form the usual relational sources? (Hadoop, NoSQL, unstructured, etc.)offer for NoSQL databases? u How do you accommodate the IoT?
  • 33. u If you do not do MDM already, how do you start and what are the immediate business benefits? u Do you model data flows (consider, for example, real-time analytics)? u Where do you see current/future competition emerging from in the modeling or governance market?